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"Gempa bumi, salah satu bencana alam yang sering terjadi di Indonesia dan sering memakan banyak korban jiwa. Sistem peringatan dini terbukti efektif untuk mengurangi jumlah korban jiwa akibat gempa bumi ataupun bencana susulan lainya. Perangkat Internet-of-things berbasis seluler dapat digunakan sebagai perangkat peringatan dini untuk mendeteksi gempa bumi.Ketika banyak perangkat pendeteksi kejadian secara bersamaan melakukan transmisi ke jaringan seluler maka akan terjadi bursty transmission yang mengakibatkan kongesti pada jaringan seluler. Kongesti yang terjadi mengakibatkan peningkatan delay dan penuruan success probability pada prosedur random access jaringan seluler yang dapat mengurangi efektifitas sistem deteksi dini. Pengaturan preamble pada access class dapat digunakan untuk mengurangi dampak kongesti yang terjadi.

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Earthquakes, one of the natural disasters that often occur in Indonesia and often take many lives. The early warning system has proven to be effective in reducing the number of casualties due to earthquakes or other aftershocks. Cellular-based Internet-of-things devices can be used as early warning devices to detect earthquakes. When multiple incident detection devices are simultaneously transmitting to the cellular network, bursty transmission will occur, resulting in congestion on the cellular network. The congestion that occurs results in an increase in delay and a decrease in success probability in random access cellular network procedures which can reduce the effectiveness of the early detection system. Preamble settings on access classes can be used to reduce the impact of congestion that occurs."

"Pada penelitian ini membahas sistem pemantauan pada stairlift menggunakan internet of things (IoT), di mana sistem tertanam dalam fisik stairlift menggunakan sensor yang dipasang pada komponen stairlift dan kemudian diintegrasikan ke dalam platform IoT cloud (thingspeak) melalui jaringan internet. Akuisisi data fisis multi-sensor dapat berjalan, banyak informasi yang dapat diakses seperti: temperature motor, kecepatan, beban penumpang, konsumsi daya, getaran bearing dan getaran motor. Sistem pemantauan dapat berjalan secara real time, sehingga membuat pemantauan terpusat dan kegagalan operasi stairlift dapat dicegah sedini mungkin melalui early warning system (EWS) via Telegram. Selain itu, sistem ini dapat memberikan dukungan analisis teknis dalam mengembangkan prototype stairlift di masa mendatang. Berdasarkan analisis hasil pemantauan yang diperoleh, prototype stairlift layak dikembangkan untuk skala industri, secara operasional memenuhi ASME A18.1, ISO 10816 dan ISO 2372. Hal ini ditunjukkan dalam ujicoba variasi beban penumpang hingga maksimum 115 kg diperoleh kecepatan maksimum rata-rata <0,2 m/s, temperature motor <74,6 ˚C, konsumsi daya <600 watt, acceleration getaran bearing <0,5 g'peak dan kecepatan getaran motor (RMS) <4,5 m/s. Namun masih dibutuhkan improvement pada sistem teknis operasional prototype stairlift diantaranya temperature motor, konsumsi daya dan kecepatan agar dapat berjalan stabil.

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This research discusses monitoring systems on stairlift using internet of things (IoT), where the system embedded in the physical stairlift uses sensors that are mounted on the stairlift component and then integrated into the IoT cloud platform (thingspeak) via the internet network. Multi-sensor physical data acquisition can run, a lot of information that can be accessed such as: motor temperature, speed, passenger load, power consumption, bearing vibration and motor vibration. The monitoring system can run in real time, thus making centralized monitoring and failure of stairlift operations preventable as early as possible through the early warning system (EWS) via Telegram. In addition, this system can provide technical analysis support in developing stairlift prototypes in the future. Based on the analysis of the monitoring results obtained, the prototype stairlift is suitable for industrial scale development, operationally compliant with ASME A18.1, ISO 10816 and ISO 2372. This is shown in the trial of passenger load variations up to a maximum of 115 kg obtained an average maximum speed <0, 2 m/s, motor temperature <74.6˚C, power consumption <600 watts, bearing vibration acceleration <0.5 g'peak and motor vibration speed (RMS) <4.5 m/s. However, improvements are still needed in the operational technical system of the prototype stairlift including motor temperature, power consumption and speed so that it can run stably."

"[ABSTRAKKrisis rupiah di tahun 1997-1999 adalah gejolak besar bagi ekonomi Indonesia dimana pertumbuhan ekonomi anjlok, sistem perbankan lumpuh, diikuti dengan konsekuensi sosial dan politik yang pahit. Diyakini jika otoritas moneter daan pembuat kebijakan lainnya dapat mengantisipasi krisis tersebut, segala konsekuensi yang tidak diinginkan tersebut dapat dikurangi dan mungkin dihindari.Sistem pendeteksian dini untuk krisis rupiah diharapkan dapat memberi waktu para pembuat kebijakan untuk mengantisipasi datangnya krisis. Namun salah satu faktor sukses yang kritikal dalam pembentukan sistem tersebut adalah seleksi indikator.Penelitian ini menerapkan Indication & Warning Intelligence (I&W)untuk sistem pendeteksian dini krisis rupiah dengan produk akhir berupa set indikator yang dianggap efektif memprediksi krisis rupiah. Dengan I&W diseleksi lima indikator, yang selanjutnya diuji keefektifannya dengan metode regresi logit, yang menyatakan bahwa terdapat tiga indikator yang dapat memprediksi krisis rupiah secara signifikan yang adalah: 1) Real Effective Exchange Rate, 2) Deposit Money Bank?s Foreign Asset Growth, dan 3) Inflasi.

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ABSTRACTCurrency crisis in Indonesia that took place in 1997-1999 was a major shock to the economy which plunge the growth, collapse the banking system with its bitter social and political consequences. It is acknowledged that if the monetary authority could anticipate such shock, the result would not be as devastating, it could be far lessen, and hopefully avoided.Early warning system for currency crisis is crucial for policy makers to anticipate the coming crisis with enough preparation. One of the most important factor in framing that system is indicators selection.This research apply Indication & Warning Intelligence (I&W) to early warning system of rupiah crisis with indicator set as its end product. With I&W five indicators are selected, which underwent further significance test with logistic regression method. This method results in three indicators being the most effective in predicting rupiah crisis, namely: 1) Real Effective Exchange Rate, 2) Deposit Money Bank?s Foreign Asset Growth, and 3) Inflation.;Currency crisis in Indonesia that took place in 1997-1999 was a major shock to the economy which plunge the growth, collapse the banking system with its bitter social and political consequences. It is acknowledged that if the monetary authority could anticipate such shock, the result would not be as devastating, it could be far lessen, and hopefully avoided.Early warning system for currency crisis is crucial for policy makers to anticipate the coming crisis with enough preparation. One of the most important factor in framing that system is indicators selection.This research apply Indication & Warning Intelligence (I&W) to early warning system of rupiah crisis with indicator set as its end product. With I&W five indicators are selected, which underwent further significance test with logistic regression method. This method results in three indicators being the most effective in predicting rupiah crisis, namely: 1) Real Effective Exchange Rate, 2) Deposit Money Bank’s Foreign Asset Growth, and 3) Inflation., Currency crisis in Indonesia that took place in 1997-1999 was a major shock to the economy which plunge the growth, collapse the banking system with its bitter social and political consequences. It is acknowledged that if the monetary authority could anticipate such shock, the result would not be as devastating, it could be far lessen, and hopefully avoided.Early warning system for currency crisis is crucial for policy makers to anticipate the coming crisis with enough preparation. One of the most important factor in framing that system is indicators selection.This research apply Indication & Warning Intelligence (I&W) to early warning system of rupiah crisis with indicator set as its end product. With I&W five indicators are selected, which underwent further significance test with logistic regression method. This method results in three indicators being the most effective in predicting rupiah crisis, namely: 1) Real Effective Exchange Rate, 2) Deposit Money Bank’s Foreign Asset Growth, and 3) Inflation.]"

"ABSTRAKSetiap permintaan koneksi yang terjadi di jaringan seluler perlu melewati prosedur random access. Dalam jaringan 5G, terdapat minimal tiga layanan yaitu mMTC, eMBB, dan URLLC. Setiap layanan yang ada pada jaringan 5G dapat dibagi-bagi resource yang ada dengan menggunakan teknologi network slicing. Resource yang digunakan dalam prosedur random access yaitu preamble. Preamble dikonfigurasikan untuk setiap slice agar mencapai kebutuhan tiap layanan yang ada. Selain preamble, performa dari jaringan ditentukan oleh jumlah transmisi dan durasi backoff window, semakin besar kedua variabel tersebut maka jumlah perangkat yang dapat dilayani akan semakin banyak. Tetapi semakin banyak jumlah transmisi dapat menyebabkan borosnya daya yang dipakai dan semakin lama durasi backoff window menyebabkan delay yang tinggi. Pengaturan jumlah preamble, jumlah transmisi, dan lamanya backoff window pada masing-masing slice diperlukan untuk memenuhi kriteria yang diperlukan agar mendapatkan performa yang baik.

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ABSTRACT

Every connection request that occurs on a cellular network needs to go through a random access procedure. In the 5G network, there are at least three services namely mMTC, eMBB, and URLLC. Every service on the 5G network can be shared with existing resources using network slicing technology. The used resource in the random access procedure is preamble. The preamble is configured for each slice to meet the needs of each existing service. Besides the preamble, the performance of the network is determined by the number of transmissions and the duration of the backoff window, the larger the two variables, the more devices can be served. But the greater number of transmissions can increase power usage and the longer duration of the backoff window causes a high delay. Setting the number of preambles, the number of transmissions, and the length of backoff window in each slice is needed to meet the criteria needed to get good performance.

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"ABSTRAK

Sistem deteksi intrusi merupakan sistem peringatan ketika ada percobaan serangan pada jaringan komputer, dengan memberikan informasi log aktivitas mencurigakan yang dapat dianalisis dan ditindaklanjuti dalam bentuk respon untuk melindungi sistem dari ancaman sebelum menyebabkan dampak lebih besar. Secara teknis, penentuan prioritas penanganan intrusi berdasarkan pada severity yang ditentukan oleh sistem atau denganskor kerentanan. Namun ada hal lain yang menjadi isu, yaitu urgensi dari sektor strategis sebagai pertahanan nasional dalam pengamanan fasilitas, jaringan, aset berbasis informasi dan fisik yang diatur oleh suatu negara dengan menetapkan sektor strategis sebagai sektor prioritas yang wajib dilindungi saat terjadi insiden sebelum berdampak lebih besar.

Pada penelitian ini, kami melakukan evaluasi beberapa metode penentuan prioritas yang diimplementasikan pada model respon yang digunakan, yaitu berdasarkan konsep manajemen waktu 4 kuadran yang telah digunakan oleh peneliti sebelumnya dengan data pengujian berupa data intrusi berbasis snort. Metode penentuan respon yang dievaluasi antara lain metode severity berdasarkan sistem deteksi intrusi berbasis snort yang disebut snort priority, rating threshold yaitu skor kerentanan, dan metode perhitungan indikator & kriteria (critical & urgent). Seiring dengan urgensi dari sektor strategis, maka pada pengujiannya metode indikator & kriteria dititik beratkan pada target yang terdaftar sebagai sektor strategis.

Penelitian ini menyimpulkan bahwa metode indikator dan kriteria sebagai faktor penentu prioritas lebih detil sehingga cukup efektif untuk diterapkan dengan model respon pada data pengujian. Selain itu, dengan metode snort priority dan rating threshold penentuan prioritas tidak memperhatikan apakah target intrusi merupakan sektor strategis atau bukan karena prioritas berdasarkan pada dampak yang telah didefinisikan oleh sistem. Namun dengan metode perhitungan indikator dan kriteria, faktor penting yang melibatkan target sektor strategis dapat didefinisikan sebagai salah satu indikator prioritas untuk menentukan kriteria critical sehingga penanganan intrusi dapat diprioritaskan lebih tinggi.

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ABSTRACT

The intrusion detection system is a warning system when there is an attempted attack on a computer network. It provides suspicious activity log information that can be analyzed and acted on in the form of a response to protect the system from threats before causing a more significant impact. Technically, determining the priority of intrusion handling is based on severity determined by the system or vulnerability scoring. However, some issues become internal issues. A country regulates the urgency of the critical sector as a national defense in securing information-based and physical facilities, networks, and assets by establishing the critical sector as a priority sector that must be protected when an incident occurs before it has a more significant impact.

In this study, we evaluated some priority determination methods implemented in the response model used, based on the 4-quadrant time management concept used by previous researchers with test data in the form of snort-based intrusion data. The response determination methods evaluated include severity based on a snort-based intrusion detection system called snort priority, rating threshold, i.e., vulnerability score, and the purpose of calculating indicators & criteria (critical & urgent). Along with the urgency of the critical sector, the testing of indicator methods and criteria has emphasized on the targets listed as critical sectors.

This study concludes that indicator methods and criteria as determinants of priorities are more detailed so that they are effective enough to apply with response models in test data. Besides, the snort priority method and the threshold rating determination of priorities do not pay attention to whether the intrusion target is a critical sector or not because of the priority based on the impact that has been defined by the system. But with the method of calculating indicators and criteria, important factors involving critical sector targets can be identified as one of the priority indicators to determine critical criteria so that intrusion will be handling prioritized."

"Banjir merupakan bencana besar yang kerap kali melanda Indonesia. Jakarta sebagai Ibukota beberapa tahun sekali mengalami banjir besar ini. Salah satu wilayah yang mengalami dampak negatif bencana ini ialah Kampung Melayu, Jakarta Timur. Terletak dekat daerah aliran sungai Ciliwung, bencana banjir di Kampung Melayu tak terelakkan terjadi. Penelitian ini bertujuan untuk membuat sistem deteksi bencana banjir di Kampung Melayu dengan implementasi algoritma Random Forest. Temperatur, tekanan udara, intensitas curah hujan, intensitas radiasi sinar matahari dan kelembaban relatif dari Stasiun Cuaca Citeko digunakan sebagai dataset dengan data tambahan berupa tinggi muka air di Pos Air Bendung Katulampa, Pos Air Depok dan Pos Air Manggarai. Hasil prediksi berupa empat kelas klasifikasi status siaga banjir dari tiap pos. Selain menggunakan algoritma Random Forest, penelitian ini juga menggunakan algoritma Decision Tree sebagai pembanding untuk melihat kinerja terbaik dari keduanya. Kedua algoritma ini merupakan metode yang kerap kali digunakan untuk pemodelan data time -series. Random Forest mencapai akurasi sebesar 99,17% dan Decision Tree mencapai 98,90%. Hasil ini menunjukkan bahwa sistem deteksi bencana banjir di Kampung Melayu, Jakarta dapat bekerja lebih baik dengan pengimplementasian Random Forest.

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Flooding is a severe disaster that happens frequently in Indonesia. Jakarta, as the capital city of Indonesia, experiences this big flood every few years. One of the areas which experienced the negative impact of this disaster was Kampung Melayu, East Jakarta. Located near the Ciliwung river basin, flooding in Kampung Melayu is inevitable. The research aims to create a flood detection system in Kampung Melayu with the implementation of the Random Forest algorithm. Temperature, air pressure, rainfall, solar radiation, and relative humidity from the Citeko Weather Station were used as datasets with the addition of water level at the Katulampa Dam Water Post, Depok Water Post, and Manggarai Water Post. Prediction results in the form of four classes of flood alert status classification from each water post. In addition to using the Random Forest algorithm, this research also uses the Decision Tree algorithm as a comparison to see the best performance of the two algorithms. Both algorithms are methods which often used for time – series data modelling. Random Forest achieved 99,17% accuracy and Decision Tree achieved 98,90%. These results show that the flood detection system in Kampung Melayu, Jakarta can work better with the implementation of Random Forest. "

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Kondisi pasien selama di Instalasi Gawat Darurat dapat mengalami perburukan, sehingga perlu melakukan deteksi dini dengan Early Warning Score yang harus dilaksanakan sesuai dengan Standar Prosedur Operasional. Penelitian ini bertujuan untuk mengetahui persepsi perawat mengenai kesesuaian pelaksanaan Early Warning Score. Penelitian deskriptif ini menggunakan purposive sampling yang melibatkan 70 perawat di Instalasi Gawat Darurat. Hasil penelitian ini menunjukkan bahwa 44 perawat (62,9%) telah mempersepsikan dirinya melaksanakan Early Warning Score sesuai Standar Prosedur Operasional. Penelitian ini diharapkan dapat menjadi data awal untuk penelitian selanjutnya dan menjadi evaluasi bagi perawat dalam pelaksanaan Early Warning Score sesuai Standar Prosedur Operasional di Rumah Sakit.

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The condition of the patient while in the Emergency Department can be deteriorating, so it is necessary to conduct early detection with an Early Warning Score which must be carried out in accordance with Standard Operating Procedures. This study aims to determine the perceptions of nurses regarding the suitability of the implementation of Early Warning Score. This descriptive study used purposive sampling involving 70 nurses in the Emergency Department. The results of this study indicate that 44 nurses (62.9%) have perceived themselves to be implementing Early Warning Score according to the Standard Operating Procedures. This research is expected to be preliminary data for further research and can be used as evaluation for nurses in implementing Early Warning Score according to the Standard Operating Procedures at the hospital.

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"Banjir di Jakarta merupakan masalah yang kompleks yang dipengaruhi oleh kombinasi faktor geografis, sosial, ekonomi, dan lingkungan. Studi ini berfokus pada prediksi banjir dengan membandingkan data stasiun darat Automatic Rain Gauge (ARG) dan data satelit Climate Hazards Group InfraRed Precipitation (CHIRPS) menggunakan Adaptive Neurofuzzy Inference System (ANFIS) yang terintegrasi dengan Principal Component Analysis (PCA). Dataset mencakup pengukuran curah hujan dari ARG dan CHIRPS, serta data ketinggian air dari tahun 2014 hingga 2020. ARG menyediakan data curah hujan lokal yang akurat, sementara CHIRPS menawarkan cakupan curah hujan regional yang luas. Teknik praproses seperti imputasi rata-rata, normalisasi data, dan metode interquartile range (IQR) digunakan untuk meningkatkan kualitas data. Model ANFIS-PCA, yang mengintegrasikan logika fuzzy dan pelatihan jaringan saraf tiruan, diterapkan dengan pembagian data 80:20 untuk pelatihan dan validasi. Ketika dilatih dengan data stasiun darat ARG dan pengukuran ketinggian air, model ANFIS-PCA menunjukkan akurasi yang superior, dengan root mean square error (RMSE) sebesar 0,13, mean absolute error (MAE) sebesar 0,12, dan R² sebesar 0,82. Sebaliknya, model ANFIS tanpa PCA menghasilkan kesalahan yang lebih tinggi, dengan RMSE 6,3, MAE 6,2, dan R² 0,74. Pelatihan dengan data satelit CHIRPS menghasilkan kesalahan yang jauh lebih tinggi (RMSE 30,14, MAE 24,05, R² 0,42). Sedangkan hasil ANFIS – PCA menghasilkan akurasi yang lebih bagus (RMSE 4,8, MAE 2,0 dan R² 0,55) . Hasil penelitian menunjukkan bahwa ANFIS-PCA memiliki kinerja yang lebih baik dibandingkan model ANFIS tanpa PCA, terutama ketika dilatih dengan data dari stasiun darat. Integrasi PCA berhasil mengurangi dimensi data, meningkatkan efisiensi komputasi dan akurasi model. Selain itu hasil ini juga menegaskan keunggulan pengukuran curah hujan data ground station untuk prediksi banjir, mempunyai angka presisi yang lebih tinggi dan kerentanan yang lebih rendah terhadap kesalahan dibandingkan data satelit. Sementara itu data satelit CHIRPS menawarkan cakupan spasial yang lebih luas.

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Flooding in Jakarta is a complex issue influenced by a combination of geographical, social, economic, and environmental factors. This study focuses on flood prediction by comparing ground station data from Automatic Rain Gauges (ARG) and satellite data from the Climate Hazards Group InfraRed Precipitation (CHIRPS) using the Adaptive Neuro-Fuzzy Inference System (ANFIS) integrated with Principal Component Analysis (PCA). The dataset includes rainfall measurements from ARG and CHIRPS, as well as water level data from 2014 to 2020. ARG provides accurate local rainfall data, while CHIRPS offers broad regional precipitation coverage. Preprocessing techniques such as mean imputation, data normalization, and the interquartile range (IQR) method were employed to enhance data quality.

The ANFIS-PCA model, which integrates fuzzy logic and neural network training, was implemented using an 80:20 data split for training and validation. When trained with ARG ground station data and water level measurements, the ANFIS-PCA model demonstrated superior accuracy, achieving a root mean square error (RMSE) of 0.13, mean absolute error (MAE) of 0.12, and R² of 0.82. In contrast, the ANFIS model without PCA yielded higher errors, with RMSE of 6.3, MAE of 6.2, and R² of 0.74. Training with CHIRPS satellite data resulted in significantly higher errors (RMSE 30.14, MAE 24.05, R² 0.42). Meanwhile, the ANFIS-PCA model trained on combined datasets showed improved performance, achieving RMSE of 4.8, MAE of 2.0, and R² of 0.55.

The results indicate that the ANFIS-PCA model outperforms the ANFIS model without PCA, particularly when trained with ground station data. The integration of PCA successfully reduced data dimensionality, improving computational efficiency and model accuracy. Furthermore, the findings reaffirm the superiority of ground-based measurements for flood prediction due to their higher precision and lower susceptibility to errors compared to satellite-derived data, while CHIRPS satellite data offers wider spatial coverage."

"Early Warning System EWS merupakan alat skoring yang digunakan untuk memantau kondisi pasien di ruang perawatan maupun di Instalasi Gawat Darurat IGD. Pada IGD yang cenderung overcrowded dan memiliki arus perpindahan pasien yang lambat penggunaan EWS digunakan untuk memantau kondisi pasien melalui tanda-tanda vital sehingga perburukan kondisi pasien dapat segera dikenali. Tujuan dari penelitian ini untuk mengetahui hubungan tingkat pengetahuan perawat tentang initial assessment dengan penatalaksanaan EWS. Penelitian ini merupakan penelitian kuantitatif dengan metode deskriptif korelatif dengan desain cross-sectional yang dilakukan kepada 70 perawat IGD. Hasil menunjukan adanya hubungan yang bermakna antara tingkat pengetahuan perawat terhadap initial assessment dengan penatalaksanaan EWS di IGD p= 0.001 yang menunjukan semakin tinggi tingkat pengetahuan perawat tentang EWS yang terdapat dalam initial assessment maka penatalaksanaan EWS yang dilakukan semakin baik, sehingga peningkatan pengetahuan melalui pelatihan perlu ditingkatkan agar penatalaksanaan EWS yang baik dapat dilaksanaakan secara menyeluruh.

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Early warning system EWS is a physiological scoring to observe the patients condition not only in hospital wards but also in Emergency Department ED. At an overcrowded ER that have slow of patient flow, EWS is use as an early detection of patients deterioration by observing the vital signs.

The purpose of this study is to identify the relationship between nurses knowledge of initial assessment and the application of EWS at emergency department. This is a quantitative study that used descriptive correlative with cross sectional design toward 70 emergency nurses.

The result showed there is a relationship between Nurses Knowledge of Initial Assessment and The Use of Early Warning System at Emergency Room p 0 .001 that show that the higher the level of nurses knowledge, their behavior is better. It is recommended to maintain the use of EWS in ED that already good through training regularly re sertification."